Global estimates of transmission intensity and short-term forecasts during the COVID-19 pandemic
Challenges, impacts and implications for the future
https://epiforecasts.io
The answer to the question is not always obvious
30 January, 2020
2 March, 2020
August 2020
Generative Bayesian model
\[ \begin{align} \textrm{New infections}~I(t) & = R_t \sum_{\tau} g_{\tau} I_{t-\tau}\\ \textrm{Reproduction number}~R(t) & = R_{t-1} \exp(\textrm{GP})\\ \textrm{Delayed reporting}~D(t) & = \sum_\tau \xi_\tau I_{t-\tau} \\ \textrm{Observations}~C(t) & = \mathrm{NegBin}(D_t \omega_{(t \textrm{mod} 7)}, \phi) \end{align} \]
Implemented in stan.
High-performance computing
R package for \(R_t\) estimation and forecasting
R package for (sub-)national data
Subnational estimates
Daily estimates stored on github
Challenge #1: Methodology
Originally used methodology that ignored delays.
Challenge #2: Lack of local context
Challenge #3: Communication
Challenge #4: Multiplication of effort
Challenge #5: Capacity
Impact #1: Use by other researchers
Impact #2: Use by ourselves
